Scotland wide data

Cancer incidence between sexes across Scotland

ggplotly(nationwide_cancer %>% 
  filter(sex != "All",
         health_board == "scotland_wide",
         cancer_site == "All cancer types") %>% 
  ggplot()+
  aes(x = year, y = crude_rate, colour = sex)+
    labs(x = "Year", y = "Crude rate", title = "Cancer incidence between men and women")+
  geom_line())

Cancer incidence by health board in Scotland

ggplotly(nationwide_cancer %>% 
  filter(sex == "All",
         cancer_site == "All cancer types") %>% 
  ggplot()+
  aes(x = year, y = crude_rate, colour = health_board)+
    labs(x = "Year", y = "Crude rate", title = "Crude rate of all cancer types per health board")+
  geom_line()+
  theme_minimal())

Top 5 cancer incidence by cancer type in the past 24 years

ggplotly(nationwide_cancer %>% 
  filter(sex == "All",
         cancer_site != "All cancer types",
         health_board == "scotland_wide") %>% 
    group_by(year) %>% 
  slice_max(crude_rate, n = 3) %>% 
ggplot()+
  aes(x = year, y = crude_rate, fill = cancer_site)+
   theme(legend.position = "none")+
    labs(x = "Year", y = "Crude rate", title = "Top 5 highest incidences of cancer")+
  geom_col())

Cancer incidence in the Borders compared to Scotland wide data

Cancer incidence in the Borders compared to Scotland as a whole

ggplotly(nationwide_cancer %>%
 filter(sex %in% "All",
        cancer_site == "All cancer types",
        health_board %in% c("scotland_wide", "NHS Borders")) %>%
 ggplot() +
 aes(x = year, y = crude_rate, colour = health_board) +
 geom_line(size = 1L) +
  geom_smooth(span = 0.75)+
  labs(x = "Year", y = "Crude rate", title = "Cancer incidence in the Borders compared to Scotland wide")+
 scale_color_hue() +
 theme_minimal())
## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

### The top 5 highest incidences of cancer per health board over the years (1994 - 2018)

nationwide_cancer %>% 
  filter(cancer_site == "All cancer types",
         sex == "All") %>% 
  group_by(year) %>% 
  slice_max(crude_rate, n = 5) %>% 
  select(year, health_board, crude_rate)
## # A tibble: 125 x 3
## # Groups:   year [25]
##     year health_board                  crude_rate
##    <dbl> <chr>                              <dbl>
##  1  1994 NHS Western Isles                   577.
##  2  1994 NHS Greater Glasgow and Clyde       548.
##  3  1994 NHS Dumfries and Galloway           539.
##  4  1994 NHS Highland                        525.
##  5  1994 NHS Fife                            518.
##  6  1995 NHS Dumfries and Galloway           559.
##  7  1995 NHS Borders                         554.
##  8  1995 NHS Greater Glasgow and Clyde       549.
##  9  1995 NHS Tayside                         532.
## 10  1995 NHS Lothian                         517.
## # … with 115 more rows

When this table is graphed, it is clear that NHS Borders is present in the top 5 21 out of the 24 years recorded

nationwide_cancer %>% 
  filter(cancer_site == "All cancer types",
         sex == "All") %>% 
  group_by(year) %>% 
  slice_max(crude_rate, n = 5) %>% 
  ggplot()+
  aes(x = year, y = crude_rate, fill = (health_board == "NHS Borders"))+
  scale_fill_discrete(name = " ", labels = c("Other health boards", "NHS Borders"))+
  labs(x = "Year", y = "Crude rate", title = "Top 5 Health Boards with Highest Incidence of Cancer in Scotland")+
  geom_col()

# Cancer data in the Borders

Breakdown of cancer incidence in the Borders by type of cancer, over time

ggplotly(borders %>%
 filter(sex %in% "All",
        cancer_site != "All cancer types") %>%
 ggplot() +
 aes(x = year, y = crude_rate, colour = cancer_site) +
 geom_line(size = 1L) +
 scale_color_hue() +
   labs(x = "Year", y = "Crude rate", title = "Cancer incidence in the Borders", fill = "Type of cancer")+
 theme(legend.position = "none"))
ggplotly(borders %>% 
  filter(sex != "All",
         cancer_site != "All cancer types") %>% 
    group_by(year, sex) %>% 
  slice_max(crude_rate, n = 3) %>% 
  ggplot()+
  aes(x = year, y = crude_rate, colour = cancer_site)+
  facet_wrap(~ sex)+
   theme(legend.position = "none")+
    labs(x = "Year", y = "Crude rate", title = "Top 3 highest incidences of cancer for men and women over the years")+
  geom_line())